Various imaging, scanning, and detection techniques for detecting and quantifying fluorescent labels are known in the art. These techniques differ in their detection capability, speed, and cost, but a common challenge to all fluorescence imaging techniques is the separation of the light used to excite the targeted fluorophores from the emitted fluorescence light. One common method uses a combination of dichroic filters/beamsplitters and band-pass filters that isolate the fluorescence light for detection. This approach is characterized by limitations as to the number of separate fluorescence emissions and the number of detection channels that can be used in the same system in parallel. Significantly, this approach requires fixed band-pass filters and thus cannot be easily changed to adapt to variations in the wavelength(s) of the fluorescence light being detected.
Another approach is to use a tunable band-pass filter, either between the emitted fluorescence and the detector, or in front of the illuminating source. For example, U.S. Pat. No. 5,379,065 discloses a spectral imaging device for use on a space vehicle to acquire terrestrial surface images, wherein a spectrally agile filter (SAF) is used to rapidly change the spectral pass band to acquire multiple images through different spectral filters; U.S. Pat. No. 6,690,466 discloses a spectral illuminator that is controlled to selectively emit light in each of a number of independent wavelength bands. The type of filter used and the tuning method depends on the speed of tunability, insertion loss, and whether the imaging method is a point imager or an area imager. The tunable band-pass filter approach falls into the multi-spectral class if the operating spectral resolution is coarse (e.g., on the order of tens of nanometers) and into the hyperspectral class if it has a much higher spectral resolution (e.g., in the sub-nanometer range) U.S. Pat. Nos. 6,495,363 and 6,495,818 provide examples of hyperspectral filtering. In the prior art hyperspectral methods, the data is processed post image acquisition.
The tunable band-pass filter approach requires the use of at least one tunable filter per detection channel, and measurements need to be taken at each spectral position (i.e., wavelength band) sequentially. Unless the technology used in tuning the filter is fast, this approach tends to be slow, particularly if higher spectral resolution is needed. Of the various tunable filters implemented in fluorescence filtering, the fastest are Liquid Crystal (LC) filters as disclosed in the '466 patent, and Acousto-Optic filters (AOTF) as disclosed in the '065 patent. These filters are fast, but they are expensive and suffer from high optical insertion loss. Furthermore, since they require sequential detection band-by-band, they can result in a much slower process than the filter itself is capable of. Another type of device that can be used as a tunable filter is the spectrometer. This device can perform the function of tuning the wavelength being detected, but at a much slower speed since the mode of tuning is typically mechanical. Spectrometers also are expensive and have an even higher insertion loss than the LC or AOTF filters.
Yet another approach in fluorescence detection is the use of spectrally dispersive elements, such as gratings and prisms, to spread the spectral content of the collected light across an array detector. The desired spectral resolution and the method of imaging dictate the type of dispersive element to use. Similar to the tunable filter approach, the dispersive element approach can fall either into the multi-spectral class or the hyperspectral class depending on its targeted spectral resolution. This method further requires the use of some type of array detector. It typically uses either a linear array detector with point imaging, or an area array detector with line imaging. In both cases, one dimension of the array detector is used for wavelength distribution. For this reason, an image is acquired for one point or one line at a time. The illumination/detection device is then scanned across the target in order to build the whole two-dimensional image. An array of spectral data is acquired for each imaged point/line and stored in a host computer. The spectral filtering of the data is processed after the scan is finished. In this manner the data is available for application of various schemes of filtering, and therefore the data processing can be optimized for the desired function at hand. The dispersive element approach thus offers significant flexibility as compared to the fixed filter and the tunable filter approaches. However, because a significant amount of data is read and stored for each point or line, the speed of operation and the storage capacity required can become overwhelming, even for a small area scan. This has been one of the main reasons that this approach has not moved into commercialization.
As an example of the storage requirements for a hyperspectral operation with post-acquisition processing, we consider the case of scanning a single microscope slide (25 mm×75 mm) with 5 μm spatial resolution and 5 nm spectral resolution across a 400 nm spectral range. Assuming that line illumination is used, the entire 25 mm width of the slide is imaged at once, and the line is scanned across the 75 mm length of the slide. This means that 5000 image pixels are needed for the line in order to obtain the 5 μm spatial resolution (25 mm/5 μm) across the width, and that 15,000 lines must be scanned in order to obtain the 5 μm spatial resolution (75 mm/5 μm) across the length. So, a frame of 5000×(400 nm/5 nm)=400×103 pixels are read every line or 6×109 pixels for a microscope slide. With a 12 bit A/D conversion, this means that 9 gigabytes of storage capacity would be needed for the scan data of a single standard size microscope slide.
Consequently, there exists a need to reduce the amount of data processing and storage requirements and thereby the required speed of scanning and processing operations, in order to benefit from the flexibility of hyperspectral imaging. The present invention offers a powerful way to harness such filtering flexibilities with only minimal data manipulations and substantially reduced storage capacity requirements.